Foundations of R and Statistics

This unit introduces the R environment, package management, and core statistical concepts needed for data science workflows. Students learn to explore and visualize data, compute and interpret measures of central tendency and variation, and gain a practical introduction to sampling, hypothesis testing, and basic supervised models (decision trees and random forests) to prepare for deeper modeling and deployment in subsequent units.

Learning Objectives

• demonstrate the ability to install and configure R and packages (including shinydashboard), connect to common data sources (local files and the UCI ML Repository), and import and inspect datasets using R functions
• apply descriptive statistical methods and base R graphics to summarize, visualize, and report measures of central tendency (mean), dispersion (standard deviation, variance), and distributional shape for given datasets
• analyze sampling distributions, z-scores, and the logic of hypothesis testing (including one- and two-sample tests), interpreting p-values, confidence intervals, and types of error in context
• evaluate the components, interpretation, and appropriate use cases of basic supervised learning techniques (decision trees and random forests) and explain how interactive dashboards (shinydashboard) can present model and data insights